What is a corpus ?

• Every project involving text analysis starts with a corpus
• A collection of machine readable texts, along with metadata
• Corpus selection is a crucial task and often overlooked
• Corpus selection is sampling : identifying the population of interest
• Corpus selection is case selection : choosing documents to analyze according to research questions and interests
• What would be a corpus that would help me answer my research question ?
  • E.g.: Twitter data is fun but not useful for everything

Corpus selection and bias

• Several aspects to consider when selecting a corpus
• How exhaustive is the corpus ?
  • Can I obtain the entirety of a document type/collection?
• Think about data generation process
  • How were these texts produced?
  • How could this affect the corpus?
• Important to explore, read some of the texts
• Think about quantity of interest, what could you derive as a measure from those texts :
  • eg. How much a given topic is discussed in these texts
  • eg. How much a given sentiment is expressed in these texts

Four biases in text selection

• Resource Bias
  • “texts often better reflect populations with more ressources to produce, record, and store documents” (Grimmer, Roberts, and Stewart 2022)
  • social groups are marginalized and invisible in all societies, thus also in text
  • eg. Twitter data is not representative of the general population; it can only speak for behavior on Twitter
• Incentive Bias
  • (self-)censoring on Social Media or in official government documents is an issue (King, Pan, and Roberts 2013)
• Medium Bias
  • Twitter only allowed 140 characters until 2017 or meeting transcripts/speeches do not necessarily reflect the tonality of the actual communication
• Retrieval Bias
  • errors or skewed queries in text mining, intransparent APIs

Acquiring a corpus

• Existing corpora vs creating original corpora
• Existing corpora
  • Texts available in open data : eg. French parliament speeches
  • Texts released by other researchers : eg. Manifesto corpus, UNGA corpus, Parlspeech, EU commissioners press releases, SOTU corpus
• Collecting digital texts
  • Image to text : digitization of images through Optical Character Recognition (OCR)
  • Audio/video to text : automatic transcription
  • Application programming interface (API)
  • Web scraping

Transcribing audio and video files

• A lot of data is in audio/video format such as interviews, radio shows, speeches, podcasts, etc.
• This data can be analyzed with text analysis tools through transcription
• Potential pipeline : download videos from youtube and transcribe audio with whisper

Corpus creation with digitized texts

• Important avenues for research in the digital humanities
• Digitization of old texts, newspapers, books, archives
• Allows to create large original corpora
• Requires OCR (Optical Character Recognition) tools
  • Transforms images of texts into machine-readable text
  • Tools such as Tesseract, TrOCR

Application programming interface (API)

Advantages of APIS

• Allows for easy & legal access to data
• Get structured data that can be easily processed
• In many cases, someone has already created a package facilitating the access to the API : NY Times, Manifesto project, Bluesky, Spotify, Youtube Twitter (RIP)

Limits of APIs

• Access only to the data that the API provider wants to share
• Often limited in the number of requests you can make
• Some APIs are not free : eg. Twitter API now
• APIcalypse/Post-API Age : APIs can be closed or modified at any time
  • Facebook, or Twitter that had before extensive API access for researchers but now closed (see minet for some workarounds)

Learning how to use an API

• This course will not cover how to use APIs
• But there are many resources online, here
• Request data from API with httr2
• Parse Json or XML in R with jsonlite

What is web scraping?

Use cases

• Collecting social media data
• Collecting press releases from an organization, speeches from actors
• Extracting data from Wikipedia pages
• Automatically download hundreds of PDFs from a website
• Collect metadata from a website

How the web is written

• Web scraping involves a minimum understanding of how the web is written: what is a web page?
  • Code: interpreted by a browser (ex: Chrome, Mozilla)
• The code of a web page can be written in:
  • HTML (Hypertext markup language): structure and content
  • CSS: style (ex: font, color)
  • Javascript: functionalities, dynamic content, search, drop-down menus etc.
• Exemple from the press release of the UN Secretary General

HTML

• HTML is a markup language
• The content of the web is written in tags, which can have attributes
• Most common tags in html
  • div
  • p
  • h1, h2, h3
• Web scraping consists in extracting the content of the html source code to get certain information
• CTRL + U
• Selector Gadget

Ethics on data access

• Web scraping is about collecting data not intended for you
  • May be illegal but also exceptions for research purposes
  • Access to private data, overload servers
  • Websites can block you/track you
• Good practices :
  • API first
  • Check permissions (robot.txt)
  • Slow down scraping

Ethics on data use

Complex and dynamic pages